Presence data generally isn't real-time and its accuracy is lacking. For example, instant messaging applications, such as MSN messenger, simply sets a user's status to away if no keyboard/mouse interaction is detected in a certain timeframe. Although this information is useful under some circumstances, it fails to address the simple case where the user is reading a paper document at his/her desk. Another example would be presence systems which rely on calendar entries: even though a user is scheduled for a meeting in a specific room, there is no guarantee that he/she is actually there. This meeting may have been missed, double-booked etc.
Determination of presence on a more granular scale has been addressed in the past through schemes for determining the actual real-time location of a person. However many of such schemes are not cost effective and require specific hardware, for example RFID tags carried by individuals, with readers positioned at building entrances and/or throughout a building, costly GPS systems and the like. Further, WiFi (802.11x) systems may include tracking/triangulation schemes using specialized access points and triangulation software and/or specialized WiFi (802.11x) fingerprinting using hardware integrated into the access points, for example a Cisco Wireless Location Appliance from Cisco Systems 170 West Tasman Drive San Jose, Calif. 95134. However the use of such specialized access points adds cost and involves significant infrastructure changes to existing systems.
U.S. Pat. No. 7,149,531 to Pauli Misikangas discloses a system which addresses this problem by using the signal strengths of base stations in a wireless local-area network (WLAN) as measured by a given wireless device, to develop a probabilistic model of indicating a probability distribution for signal values of several base stations at several locations, and further using the probabilistic model and signal values at a radio interface to determine the location of the given wireless device at a later time. However, calibration of the system is specific to a particular wireless device, and if the location of another type of wireless device is to be determined, the system must be recalibrated using the other type of wireless device.
A similar approach is disclosed in “Locus: Wireless LAN Location Sensing” by Arvinder Singh and Ali Taheri, in Project Report submitted to the Worcester Polytechnic Institute and dated Jan. 19, 2004 (“Locus”). The system of locus involves recording the signal strengths of several visible wireless access points for a given number of known coordinates, for example in a building. To then later find a location of a wireless device communicating with the access points, the observed signal strengths of the access points are compared against the stored values and the closest match results in a location approximation. However, Locus is prone to false signature matches, or no match at all, as matching is performed on a per value basis (i.e. absolute received signal strength) and further uses static thresholds of signal strength for matching. Further, signal strengths from different orientations are averaged into one value for a location resulting in loss of detail, as signal strength can vary greatly with respect to orientation.